Data Science with Python & Machine Learning
Go from foundational programming to deploying predictive models in 12 intensive weeks. Master Python, Pandas, Scikit-Learn, SQL, and MLOps with live 1-on-1 code critiques and guaranteed hiring support.
Industry-Aligned 12-Week Syllabus
Designed from scratch with Senior Data Scientists from Fortune 500 enterprises. Every module ends with a code-reviewed GitHub project.
Module 1: Python Engineering & Computational Thinking (Weeks 1–2)
Master clean Python 3 idioms, data structures (lists, tuples, dicts, sets), control flow, list comprehensions, functional paradigms, and Object-Oriented Programming (classes, inheritance, dunder methods). Environment setup with Anaconda, JupyterLab, Git, and virtual environments.
Module 2: Scientific Data Wrangling with NumPy, Pandas & SQL (Weeks 3–4)
Dive into high-performance array broadcasting with NumPy. Master Pandas DataFrames, multi-level indexing, merging, pivoting, string sanitization, and handling missing values. Integrate relational database querying using PostgreSQL, window functions, and subqueries.
Module 3: Exploratory Data Analysis & Visual Storytelling (Weeks 5–6)
Learn exploratory data analysis (EDA), descriptive & inferential statistics, probability distributions, central limit theorem, and A/B hypothesis testing. Build publication-quality statistical charts using Matplotlib and Seaborn, and interactive business dashboards in Tableau.
Module 4: Supervised & Unsupervised Machine Learning (Weeks 7–9)
Implement core ML algorithms from mathematical theory to Scikit-Learn: Linear & Logistic Regression, Regularization (L1/L2), Decision Trees, Random Forests, XGBoost, K-Means Clustering, and PCA Dimensionality Reduction. Master cross-validation, precision-recall trade-offs, ROC-AUC, and hyperparameter tuning with Optuna.
Module 5: Deep Learning, NLP & Production MLOps Deployment (Weeks 10–11)
Introduction to Artificial Neural Networks and PyTorch. Natural Language Processing (tokenization, TF-IDF, sentiment classification, Hugging Face transformers). Package models into low-latency REST APIs using FastAPI, Dockerize workloads, and deploy to AWS EC2.
Module 6: Capstone Defense & Placement Preparation Sprint (Week 12)
Code reviews with Senior Architects, live capstone defense presentations, ATS-optimized resume re-engineering, GitHub portfolio curation, technical whiteboard drills, and direct referrals to hiring partners.
Capstone Projects You Build & Deploy
Graduate with 3 deployment-ready, recruiter-vetted repositories on GitHub.
Automated Financial Fraud Detection Engine
An end-to-end anomaly detection pipeline analyzing 2M+ synthetic credit card transactions. Implements SMOTE re-sampling, isolation forests, and XGBoost classifiers with sub-50ms inference latency.
Healthcare Patient Readmission Predictor
Clinical patient outcome forecasting built on clinical EHR datasets. Includes exploratory survival analysis, logistic risk scoring, and interactive Streamlit physician dashboards.
E-Commerce Dynamic Pricing & Recommendation
Collaborative filtering recommendation engine paired with price elasticity modeling. Packaged as containerized microservices ready for Kubernetes deployment.
Learn Directly from Practising Data Leaders
Our instructors are not theoretical academics — they manage production AI infrastructure at leading technology companies.
Dr. Preethi Shankar
Lead FacultyAcademic Director & Former Senior Data Scientist (IBM / Target)
PhD in Applied Machine Learning from IISc Bangalore with 12+ years of production experience in predictive analytics and large-scale data modeling. Has trained over 800+ working engineers and analysts.
Vikram Adithya
Industry MentorStaff MLOps Engineer (FinTech Unicorn)
Specializes in real-time fraud feature stores and low-latency Kubernetes model serving. Conducts bi-weekly live code audits, architecture reviews, and technical interview simulations.
Verified Career Acceleration
Our alumni transition into impactful roles across leading technology, fintech, and analytics organizations.
Technical Competencies Acquired
🐍 Programming & Math
📊 Data Manipulation & BI
🤖 Machine Learning & AI
🚀 MLOps & Deployment
Launch Your Data Science Career
Next cohort kicks off on 15 Nov 2025. Cohorts are capped at 25 learners to ensure individual 1-on-1 code reviews.
All-inclusive tuition • No hidden software or certification fees
What is Included in Your Enrollment:
- ✔ 180+ Hours of live interactive instruction & cloud sandbox access
- ✔ 12 Months full access to all session recordings & Jupyter notebooks
- ✔ ISO 9001:2015 Accredited Certification with QR digital verification
- ✔ 1-on-1 code reviews & pull request critiques with Senior Engineers
- ✔ 6 Months placement acceleration & partner interview introductions
Course Frequently Asked Questions
Everything you need to know about our Data Science with Python programme, curriculum, and career support. Have a specific question? Ask our advisors.
Is this data science course good for beginners?
Yes — we designed this course specifically for people who have never coded before. The first three weeks cover Python fundamentals before we move into data manipulation, statistics, and machine learning. The only prerequisite is basic computer literacy and a genuine curiosity about data. Over 60% of our 320 graduates had no prior coding experience.
Will I get placement support after completing the course?
Yes. Placement support starts in Week 10 and includes: resume review, LinkedIn profile audit, mock technical and HR interviews, and access to our hiring partner network of 80+ companies. Our placement team actively shares relevant openings for six months after graduation. The last batch had an 89% placement rate within 3 months.
What projects will I build during the course?
You build 5 hands-on projects: (1) Customer churn prediction using logistic regression, (2) Sales forecasting dashboard in Python, (3) NLP sentiment analyser for product reviews, (4) Image classifier using scikit-learn, (5) a capstone project of your own choosing, reviewed by an industry mentor. All projects go directly onto your GitHub portfolio.
What tools and technologies does the course cover?
Python 3.x, Jupyter Notebooks, NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn, basic SQL for data analysts, Tableau (intro), and Git/GitHub. Optional advanced modules cover TensorFlow basics, model deployment with Flask, and cloud data services on AWS.
Are there weekend batches or flexible timings?
Yes. We run three batch types: Weekday Mornings (Mon–Fri, 7–9 am IST), Weekday Evenings (Mon–Fri, 7–9 pm IST), and Weekend Intensive (Sat–Sun, 9 am–1 pm IST). Self-paced online learners get 12 months of access to all recorded sessions and can attend any live Q&A session during that period.